基于随机流网络与Markov过程的制造系统可靠性建模及维护优化

doi:10.16183/j.cnki.jsjtu.2019.345

上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (3): 229-235.doi: 10.16183/j.cnki.jsjtu.2019.345

所属专题：《上海交通大学学报》2021年12期专题汇总专辑；《上海交通大学学报》2021年“机械工程”专题

基于随机流网络与Markov过程的制造系统可靠性建模及维护优化

高英铭, 陈震, 张秀芳, 潘尔顺()

上海交通大学　机械与动力工程学院，上海　200240

收稿日期:2019-11-30 出版日期:2021-03-01 发布日期:2021-04-02
通讯作者: 潘尔顺 E-mail:pes@sjtu.edu.cn
作者简介:高英铭(1995-)，男，辽宁省鞍山市人，硕士生，主要研究方向为制造系统的可靠性建模．
基金资助:
中国博士后科学基金资助项目(2019M661532)

Reliability Modeling and Maintenance Optimization of Manufacturing System Based on Stochastic Flow Network and Markov Process

GAO Yingming, CHEN Zhen, ZHANG Xiufang, PAN Ershun()

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2019-11-30 Online:2021-03-01 Published:2021-04-02
Contact: PAN Ershun E-mail:pes@sjtu.edu.cn

摘要/Abstract

摘要：

针对制造系统中设备数量与输入的不确定性，考虑制造过程中的不良品以及不良品的返工情况，分析系统可靠性与各设备数量、设备输入的关系，建立基于随机流网络的制造系统可靠性评价模型．采用齐次Markov过程对系统衰退和维护进行状态分析，以最小化维护成本为目标，以系统可靠性为约束，建立系统设备的维护模型．通过算例分析，验证该模型的有效性和先进性．

关键词: 制造系统可靠性, 随机流网络, Markov过程, 维护

Abstract:

Aimed at the uncertainty of equipment quantity and input, the defective products and its rework in the manufacturing process are investigated. Considering the influence of the number or input of the devices on system reliability, a manufacturing system reliability evaluation model is established based on stochastic flow network. The homogeneous Markov process is used to analyze the state of system degradation and maintenance. Considering the constraint of system reliability, a systematic maintenance model is proposed to minimize maintenance cost. The results of the numerical experiment demonstrate that the proposed model is effective and advanced.

Key words: manufacturing system reliability, stochastic flow network, Markov process, maintenance

中图分类号:

TH17

高英铭, 陈震, 张秀芳, 潘尔顺. 基于随机流网络与Markov过程的制造系统可靠性建模及维护优化[J]. 上海交通大学学报, 2021, 55(3): 229-235.

GAO Yingming, CHEN Zhen, ZHANG Xiufang, PAN Ershun. Reliability Modeling and Maintenance Optimization of Manufacturing System Based on Stochastic Flow Network and Markov Process[J]. Journal of Shanghai Jiao Tong University, 2021, 55(3): 229-235.

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参考文献 17

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h	g
h	g	i=1	i=2	i=3
1	1	0	0	0
	2	0	0	0
	3	0	0	0
	4	0	0	0
2	1	0.100	0.200	0.200
	2	0	0	0
	3	0	0	0
	4	0	0	0
3	1	0.050	0.150	0
	2	0.200	0.200	0.200
	3	0	0	0
	4	0	0	0
4	1	0.025	0.100	0
	2	0.100	0.150	0
	3	0.400	0.200	0.200
	4	0	0	0

维护策略	R₁/%	R₂/%	R₃/%	R/%	w_tot×10^－3/元
S₁	99.11	96.26	99.99	98.62	2.044
S₂	90.28	96.26	98.68	90.90	6.586
S₃	90.91	95.24	96.15	89.97	3.061
S₄	93.40	96.51	99.86	95.10	5.404
S₅	94.99	97.06	99.92	96.94	5.285
S₆	92.92	96.69	99.86	94.68	6.273
S₇	97.59	96.47	99.85	98.29	3.647
S₈	97.78	96.77	99.92	98.55	3.665
S₉	96.37	96.33	99.85	97.54	3.952
S₁₀	90.91	95.24	96.15	89.97	3.061
S₁₁	98.55	97.00	99.92	98.95	4.056
S₁₂	97.07	96.29	99.85	97.92	4.449

基于随机流网络与Markov过程的制造系统可靠性建模及维护优化

Reliability Modeling and Maintenance Optimization of Manufacturing System Based on Stochastic Flow Network and Markov Process

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参考文献 17

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h	g
h	g	i=1	i=2	i=3
1	1	0	0	0
	2	4	4	5
	3	3	4	5
	4	1	4	5
2	1	0	0	0
	2	0	0	0
	3	4	4	5
	4	3	4	5
3	1	0	0	0
	2	0	0	0
	3	0	0	0
	4	4	4	5
4	1	0	0	0
	2	0	0	0
	3	0	0	0
	4	0	0	0

h	g
h	g	i=1	i=2	i=3
1	1	—	—	—
	2	0.2	0.5	0.2
	3	0.5	0.5	0.5
	4	1.0	0.5	1.2
2	1	—	—	—
	2	—	—	—
	3	0.2	0.5	0.3
	4	0.5	0.5	0.8
3	1	—	—	—
	2	—	—	—
	3	—	—	—
	4	0.2	0.5	0.5
4	1	—	—	—
	2	—	—	—
	3	—	—	—
	4	—	—	—

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h	g
h	g	i=1	i=2	i=3
1	1	0	0	0
	2	4	4	5
	3	3	4	5
	4	1	4	5
2	1	0	0	0
	2	0	0	0
	3	4	4	5
	4	3	4	5
3	1	0	0	0
	2	0	0	0
	3	0	0	0
	4	4	4	5
4	1	0	0	0
	2	0	0	0
	3	0	0	0
	4	0	0	0

h	g
h	g	i=1	i=2	i=3
1	1	—	—	—
	2	0.2	0.5	0.2
	3	0.5	0.5	0.5
	4	1.0	0.5	1.2
2	1	—	—	—
	2	—	—	—
	3	0.2	0.5	0.3
	4	0.5	0.5	0.8
3	1	—	—	—
	2	—	—	—
	3	—	—	—
	4	0.2	0.5	0.5
4	1	—	—	—
	2	—	—	—
	3	—	—	—
	4	—	—	—

h	g
h	g	i=1	i=2	i=3
1	1	0	0	0
	2	4	4	5
	3	3	4	5
	4	1	4	5
2	1	0	0	0
	2	0	0	0
	3	4	4	5
	4	3	4	5
3	1	0	0	0
	2	0	0	0
	3	0	0	0
	4	4	4	5
4	1	0	0	0
	2	0	0	0
	3	0	0	0
	4	0	0	0

h	g
h	g	i=1	i=2	i=3
1	1	—	—	—
	2	0.2	0.5	0.2
	3	0.5	0.5	0.5
	4	1.0	0.5	1.2
2	1	—	—	—
	2	—	—	—
	3	0.2	0.5	0.3
	4	0.5	0.5	0.8
3	1	—	—	—
	2	—	—	—
	3	—	—	—
	4	0.2	0.5	0.5
4	1	—	—	—
	2	—	—	—
	3	—	—	—
	4	—	—	—